Weakly Supervised Object Detection (WSOD) relies on meaningful feature maps from deep neural networks. B. Zhou et al.[1]} observed that by learning object class labels, the representation of objects in space can be learned, i.e., virtual labeling of object locations can be done through information on feature maps. For example, WSDDN[2]} uses the pre-trained model as the feature extractor, and completes the prediction of the features of the candidate regions through a two-stream network, and then uses the prediction result to filter the regions. That is, the identification is completed by the quality of the prediction of each region.
